Results Of Project Research Articles

Green Infrastructure and Urban Flooding: A Survey of Prevailing Issues and Current Modelling Approaches

Abstract Green Infrastructure (GI) is gaining attention in urban flood management for its ability to reduce peak flow and volume while providing multiple urban and environmental benefits. Climate change and urbanization intensify GI development needs but also amplify design, management, and performance challenges for local governments and stakeholders. Understanding the complex challenges of implementing GI is essential for advancing sustainable urban water management in line with the Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Previous studies have often focused on specific components of GI systems, but this research provides the first comprehensive work covering it from pre-design to post-installation—of GI implementation in metropolitan Melbourne. Through a targeted survey of local councils and water consultancies, our study bridges the gap between theoretical design and practical application. By incorporating practitioners’ real-world experiences alongside technical modelling challenges, we reveal how factors like limited real-time data and regional parameterization can affect GI performance. Our approach uniquely examines interlinked obstacles, from financial constraints to maintenance protocols, giving decision-makers valuable insights to enhance GI project outcomes. This research addresses critical knowledge gaps, such as the need for community engagement and tailored technical expertise, providing actionable solutions for improving GI planning, implementation, and sustainability. Our findings offer a practical framework for future GI initiatives, helping practitioners pre-emptively tackle challenges, optimize resource use, and establish a resilient urban infrastructure that supports clean water access, sustainable urban growth, and climate adaptation.

The AI and Quantum Era: Transforming Project Management Practices

Project management is changing drastically due to the integration of artificial intelligence (AI) and quantum computing (QC), redefining traditional methods. This study explores Quantum AI (QAI) and AI-driven solutions to tackle enduring issues, including resource inefficiencies, schedule delays, and budget overruns. These technologies significantly enhance project outcomes by leveraging predictive analytics, dynamic scheduling, and high-dimensional optimization. A comparative analysis of prominent case studies, including the Crossrail Project, East Side Access, and the Montreal Olympics, highlights the superior performance of AI and QAI techniques compared to conventional methods. The study shows that QAI can cut delays by 60%, optimize resource allocation with 83% efficiency, and eliminate cost overruns by up to 40% using Monte Carlo simulations and Failure Mode Effects Analysis. These results demonstrate that quantum artificial intelligence is a ground-breaking tool for handling intricate, interconnected project settings. Additionally, this study emphasizes how QAI is scalable and applicable across industries, especially in fields that need real-time optimization and high-dimensional data processing. The proposed hybrid quantum-classical paradigm provides practical solutions and sets a benchmark for efficiency, scalability, and risk mitigation in project management.

Evolutionary Mechanism of Trust for Public–Private Partnership Projects with Public Participation

Trust plays a pivotal role in sustaining cooperative relationships and serves as the cornerstone of successful cooperation among participants in public–private partnership (PPP) projects. A decline in trust can result in unethical behavior, fractured relationships, and even project failure. Despite its critical importance, there is an insufficiency of research exploring the evolutionary dynamics of trust, particularly from the perspective of public participation. To address this, an evolutionary game model is constructed to analyze the evolution of behavioral strategies and identify their stable state from a dynamic perspective. The model examines the influence of trust levels and public participation on cooperation, revealing that trust asymmetry negatively impacts collaborative outcomes in PPP projects. There is an optimal trust level: trust positively influences cooperation up to a certain peak, beyond which excessive trust becomes detrimental and hinders cooperative outcomes. When trust levels are equal, the selection of behavioral strategies is influenced by the initial willingness of participants. A threshold exists for this initial willingness, above which cooperation is more likely to succeed. Additionally, public participation is found to significantly enhance cooperation. These conclusions advance the theoretical advancements of trust in PPP projects and offer valuable insights for policymakers and stakeholders in addressing trust-related challenges, thereby fostering sustainable collaboration.

A Systematic and Objective Framework for Evaluating Subcontractor Performance Using Monte Carlo Simulation Coupled with the Analytic Hierarchy Process and a Linear Additive Utility Model

The general contractor (GC)–subcontractor (SC) relationship is a crucial aspect of construction supply chain management, heavily influencing project outcomes. This study investigates a method for assessing SC performance and underscores its essential role in construction projects. Traditionally, SC assessments are based on subjective evaluations, which can lead to biased decision-making. To counter this, this study introduces a comprehensive framework that employs objective indices and a systematic evaluation method. The study begins with a comprehensive literature review and expert consultations to identify key indices for SC evaluation: time, cost, quality, safety, resources, satisfaction, and leadership. A hybrid method combining Monte Carlo simulation and the Analytic Hierarchy Process (AHP) is employed to assign weights to these indices through the development of probability distributions, thereby reducing judgment uncertainty. The developed evaluation model incorporates normalization and a linear additive utility model (LAUM) to calculate a performance index (PI) that quantifies SC performance across various levels, from outstanding to poor. The normalization process is applied with three tolerance levels (high, medium, and low). A real case study with a three-scenario sensitivity analysis demonstrates the model’s effectiveness. This approach provides general contractors with a more objective and transparent assessment process, minimizing bias in evaluations.

Exploring the Drivers of Workforce Productivity: A Case Study Approach

This study investigates the critical factors affecting employee productivity within a UK-based construction company, aiming to address overlooked elements in prior research, specifically regarding salary expectations. By administering questionnaires to both direct and midlevel employees, this study identifies key productivity drivers and examines managerial and organisational gaps impacting project outcomes. Key findings reveal that truck availability, worker motivation, rework, tools, and machinery significantly influence productivity levels, aligning with similar studies in the UK and the US. Notably, this research fills a gap by highlighting salary expectations as a primary factor affecting employee turnover, a consideration absents in past studies. The study’s conclusions offer insights applicable to international construction projects, promoting cross-context learning and strategic improvements in workforce management.

Research on Concrete Crack Detection in Hydropower Station Burial Engineering Based on Quantum Particle Technology

Cracking in hydraulic buried engineering can cause localized damage or complete structural failure, potentially resulting in catastrophic project outcomes. Traditional methods for detecting cracks in hydraulic concrete buried engineering are often insufficient in terms of reliability and accuracy. With the development and application of particle-based technology, it has been widely used in the field of crack detection. This research investigates the support pier of the Yingxiuwan Hydropower Plant and the lock pier of the Yuzixi Hydropower Plant. Employing principles from quantum physics, quantum particle non-destructive detection technology is introduced to identify crack locations. A three-dimensional simulation model is constructed and verified accurately through integration with CT scanning techniques. The results demonstrate that particle detection technology effectively detects cracks in hydraulic concrete buried engineering, exhibiting minimal susceptibility to external interference. The particle detection data enable 3D visualization of cracks, accurately reflecting the conditions within embedded concrete components. This method provides a reliable and advanced technical solution for precise crack detection in concrete-embedded engineering and offers critical data for exploring crack propagation mechanisms.

Project Governance Practices: Influence on the Appropriation and Sustainability of the Values of Infrastructure Projects

Project governance practices play a central role in the performance and success of initiatives, yet their contribution to the appropriation and sustainability of values remains underexplored, particularly in the infrastructure sector. This research, focused on La Grande Alliance (LGA) in the Baie-James region, investigates the effectiveness of governance practices through a mixed-methods approach combining semi-structured interviews, evaluation reports, and stakeholder surveys. The findings highlight the critical role of participative management at every stage of the process. During the definition of values, regular consultation, delegation of responsibilities, transparency, and decentralized leadership foster their appropriation. During the awareness and integration phases, collaborative leadership strengthens stakeholder engagement. Finally, in the monitoring and evaluation phase, ethical and collaborative decision-making ensures not only the appropriation of values but also their sustainability, encompassing aspects such as durability, socio-economic impact, and ecosystem protection. In conclusion, effective governance practices establish a participatory and coherent structure, enabling stakeholders to appropriate project outcomes while ensuring the long-term benefits of infrastructure initiatives in the region.

Exploring the Application of a Co-Creation Model in Archaeological Tourism: Two Case Studies developed under TURARQ's project within the frame of Link Me Up - 1000 ideas project

This paper discusses two projects that revolve around the conceptualization of tourism products within the framework of interpreting and understanding archaeological sites through a sustainable approach as part of TURARQ's initiative. The primary objective was to explore the application of a co-creation model, specifically the Project Based Learning methodology of "Demola" with the support of the "Link me Up" project, in the field of archaeological tourism. The first project, Show Me the Past, involved the development of a mobile app that utilized an agile requirement analysis to create a prototype. The second project, Showcasing Prehistory, focused on entrepreneurship, inclusion, heritage, and sustainability, using a diagnosis of the social issues affecting the Mação territory. The outcomes of the first project included the creation of a Mobile Augmented Reality (MAR) application, which allows users to visualize virtual objects in the real environment of discovery. In the second project, empathy maps were developed, encompassing archaeotourism, the local community, theme parks, and special needs. The findings indicated that these programs generated high levels of participant satisfaction and positively impacted competency development. However, further studies are required to ascertain the number of participants who have transitioned into entrepreneurs because of their involvement in these projects.

Addressing Data Point Homogeneity and Annotation Challenges to Enhance Data-Driven Approaches: The S. aureus NorA Efflux Pump Case Study.

In this study, we analyzed publicly accessible data related to the Staphylococcus aureus NorA protein, a well-known efflux pump involved in antimicrobial resistance. Our analysis revealed several inconsistencies in data annotation, and significant issues concerning the homogeneity across datasets, which compromise the reliability of data-driven approaches aimed at identifying novel Staphylococcus aureus NorA efflux pump inhibitors (EPIs). To address these challenges, we propose a standardized pipeline for experimental procedures and data annotation, designed to enhance the consistency and quality of EPI datasets submitted to repositories, thereby increasing the utility of publicly available datasets for the discovery of potential EPIs. By implementing this framework, the findings reported herein aim to foster more reliable and reproducible research outcomes in drug discovery projects targeting NorA or other efflux pumps.

A Comprehensive Review of Machine Learning and Multi-Criteria Decision Analysis in Construction Delay Management

Construction delays remain a critical challenge globally, significantly affecting project performance metrics such as cost, schedule adherence, quality, and safety. Traditional methods like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) are widely used but lack the predictive capabilities and adaptability required for dynamic project environments. Machine Learning (ML) and Multi-Criteria Decision Analysis (MCDA) have emerged as innovative tools for addressing these limitations. ML excels in predicting delay impacts by analysing historical data and uncovering hidden patterns, while MCDA provides a structured framework for prioritizing delay factors based on their influence on project performance. This paper provides a comprehensive review of the application of ML and MCDA in construction delay management, highlighting their strengths, limitations, and potential integration. The review identifies research gaps, including the need for hybrid frameworks that combine predictive insights with decision support. It proposes future directions to develop real-time tools for delay mitigation, ultimately enhancing construction project outcomes through data-driven decision-making.

Applying the PRiSM™ Methodology to Raise Awareness of the Importance of Using Sustainable Project Management Practices in Organizations

Sustainability has become crucial in today’s business landscape. Customers, suppliers, partners, and investors are increasingly demanding that companies be aware of their impacts on the environment and society. Achieving sustainability in business operations, including social, economic, and environmental aspects, is one of the major challenges for companies today. Integrating sustainability into project management fosters the development of more sustainable and responsible projects, considering environmental, social, and economic aspects. This integration allows for benefits such as risk and operational cost reduction, strengthening of the company’s reputation, and gaining stakeholders’ trust. This study takes an exploratory approach, focusing on a pilot test to investigate how the PRiSM™ (Projects Integrating Sustainable Methods) methodology can be applied in a business context to assess the level of maturity of sustainable project management practices and thus raise awareness of the importance of these issues. PRiSM™ was developed by GPM® Global (Global Project Management, Lees Summit, MO, USA) in 2013 to help organizations integrate project processes with sustainable initiatives and it is based on the P5 Standard, which incorporates tangible tools and methods to manage the balance between finite resources, social responsibility, and delivery of sustainable project outcomes. Based on the PRiSM™ methodology and the P5 Standard (2nd Edition), a comprehensive questionnaire was developed under the Portuguese Project Management Observatory® to assist companies in assessing their performance in terms of sustainable practices, resulting in a sustainable maturity level. The questionnaire aimed to evaluate companies in four impact categories: product/process impacts, social impacts, economic impacts, and environmental impacts. The results, obtained from 30 respondents, indicated that the majority of organizations achieved medium-level ratings, with an overall average of 65%. However, some still showed unsatisfactory performance, with a minimum score of 14%, indicating there is still a long way to go for the full integration of sustainability. Based on participants’ feedback, the study found that many recognized the importance of sustainability but were unaware of how to integrate sustainability practices into their project management activities, highlighting the importance of promoting education and raising awareness about sustainable project management practices. The findings, while based on a limited sample, provide valuable initial insights into the potential of PRiSM™ to foster sustainability in project management. This research underscores the need for further studies to expand and validate these preliminary conclusions.

Application of structural equation modelling (SEM) to evaluate reworks in sustainable buildings

PurposeSustainable buildings are designed to minimise the adverse impacts of buildings on users, occupants, communities and the environment while enhancing client investment, contractors’ productivity and profit margins. However, sustainable buildings often experience significant rework. This research aims to evaluate the complex interrelationships among the causes of rework in sustainable buildings using structural equation modeling (SEM) techniques. By testing the theory and validating a framework addressing the causes of rework in sustainable buildings, the construction sector can make a meaningful contribution towards a sustainable future.Design/methodology/approachThe study developed a questionnaire comprising 24 identified causes of rework in sustainable buildings, derived from an extensive literature review and field observations. The causes were evaluated using a 4-point Likert scale, ranging from less occurrence to very high occurrence. The survey was administered to construction professionals via online platforms and direct hand delivery.FindingsThe identified causes of rework were grouped into four components through exploratory factor analysis (EFA) and subsequently validated using SEM. These components are competency, information, framework and plan. While the measurement model demonstrated robustness, the structural model indicated the need for further refinement. The study provides actionable strategies to mitigate rework, supporting the advancement of sustainable practices within the construction sector.Originality/valueThe findings of this research carry substantial theoretical and practical significance for advancing knowledge and practices in the sustainable buildings market. Theoretically, the study enriches the understanding of rework causes and their interrelationships, providing a foundation for future research. Practically, the results serve as a vital resource for stakeholders in the construction sector and offer actionable insights to enhance decision-making, improve project outcomes and foster sustainable building practices.

Do oversimplified durability metrics undervalue biochar carbon dioxide removal?

Abstract Soil amendment of biochar—the solid product of biomass pyrolysis—is one of few engineered strategies capable of delivering carbon dioxide removal (CDR) today. Quantifying CDR for biochar projects hinges critically on the durability of biochar materials once amended in soil. However, consensus on the definition of durability is still evolving, and as a result, standards developing organizations have generated a variety of different methodologies to assess the removal value of biochar projects. These methodologies primarily rely on single-parameter regression models to link the molar H/C ratio—an easily measurable bulk chemical metric—to the modeled durability of biochar materials. Specific deployment variables are not commonly considered. Thus, although H/C-based methodologies simplify project development and CDR assessment, questions remain as to how well they predict real project outcomes. Via a re-analysis of existing biochar incubation data and several case studies, we show that durability standards based on bulk compositional metrics are biased towards particular feedstocks and may not account for key environmental drivers. Without provisions for these factors, we find that existing assessment models appear to discount the removal value of biochar projects significantly. However, our conclusions rely on predictive models with important weaknesses and unknown uncertainty—pointing to a need to develop a use-aligned database. Limitations notwithstanding, our findings ultimately suggest the biochar ``durability problem'' may be an artifact of the desire to simplistically define it. To reliably credit CDR, we propose a series of recommendations, including the creation of representative distributions for current feedstocks and environmental gradients to better align experimental data with real-world practices. Further, we suggest an approach to integrate in-field measurement protocols with existing strategies to evaluate CDR value, with potential to co-generate data to guide deployment, maximize agronomic co-benefits, and improve confidence in project integrity.

Developing the Learning Curve Model to Enhance Construction Project Scheduling and Cost Estimating

Aim The aim of the study is to develop a scheduling and cost estimation model for repetitive construction units by applying the learning curve theory and to contribute to advancements in construction project management practices, promoting efficiency and competitiveness within the industry. Background Construction projects, particularly those with repetitive units like housing developments, face ongoing challenges in accurate scheduling and cost estimation. Traditional estimation methods often overlook the impact of learning effects, which can improve productivity and reduce costs as crews gain experience. Learning curve theory, widely applied in manufacturing, offers a framework to model these gains in construction settings. Integrating learning curves into project planning has the potential to enhance accuracy in forecasting timelines and budgets, ultimately improving project efficiency and resource management. Objective The objective of this study is to develop and apply a learning curve model to enhance scheduling and cost estimation in repetitive construction projects, particularly in a multi-unit housing project. Methods By incorporating historical data and analyzing critical factors that impact project duration and cost, a more reliable forecasting model is developed. The learning curves are created using a three-point approach, supported by artificial neural networks (ANN) and the relative importance index (RII), to systematically assess cost divisions and influential project factors. Results The results indicate that the learning curve model can achieve time savings of 27% and labor cost savings of 36% compared to traditional estimation methods that do not consider the effect of the learning curve in construction projects. Conclusion This research demonstrates that learning curve models, combined with advanced data analysis techniques, provide a robust framework for optimizing project schedules and budgets, ultimately leading to more efficient resource utilization and cost-effective project outcomes. In other words, the study presented in this paper is significant as it can lead to improved project outcomes, cost savings, better resource management, and overall advancement in the construction industry's practices and competitiveness. This approach allows for accurate scheduling and cost forecasting based on data-driven insights.

Impact of Urban Flood Management on the Values of Properties in Ibadan, Oyo State

This study investigated the impact of the Ibadan Urban Flood Management Project (IUFMP) on property values in 17 priority project sites across Ibadan, Oyo State, Nigeria. Urban flooding has been a long-standing challenge in Ibadan, causing substantial damage to property and infrastructure, particularly in flood-prone areas, which has led to declining property values. In response to the 2011 floods, the Oyo State government, with World Bank support, initiated the IUFMP to mitigate flood risks through drainage construction, river channelization, and early warning systems. A survey research design was used, combining both quantitative and qualitative data collection methods. Primary data was gathered from 383 respondents, including homeowners, tenants, estate surveyors, and key project officials. Quantitative data were analyzed using descriptive and inferential statistics, while key informant interviews provided insights into project sustainability and management. The study found that 54.8% of respondents strongly disagreed that the IUFMP led to an increase in capital property values, with a mean score of 1.82, and 53% strongly disagreed that rental values increased, with a mean score of 1.89. Furthermore, 66.8% of respondents disagreed that property purchases had increased (mean: 1.48), and 74.4% disagreed that the filthiness in the areas had reduced (mean: 1.26). Thematic insights from key informant interviews revealed positive project outcomes, including improved flood resilience and enhanced livelihoods. However, significant challenges in fostering community engagement and ensuring sustainable environmental benefits were identified. The study concludes that while the IUFMP has successfully reduced flooding and improved infrastructure, its impact on property value appreciation remains limited. It recommends sustainable strategies, including enhanced community involvement, to maximize the long-term benefits of the project.

Improving health outcomes by strengthening public sector capacity in social and behaviour change programming in Nigeria: a qualitative study

ObjectiveThis paper examines the outcomes of the public sector capacity strengthening (PSCS) approach and how they contribute to the promotion of maternal, newborn, and child health, and nutrition (MNCH+N), family...

Audience Listening as a Path for Public Service Media: The Case of “La Gran Consulta” in Spain

ABSTRACT In an era marked by disinformation and eroding trust in social institutions, public service media (PSM) face significant challenges in audience engagement and redefining their role within the digital media ecosystem. “La Gran Consulta” (LGC) [The Great Consultation] emerges as a groundbreaking initiative by Radio Televisión Española (RTVE) aimed at involving the public in shaping the future of national broadcasting and its digital transformation. This study critically examines LGC as a paradigm of effective audience listening, employing the theoretical framework of the architecture of listening. Through primary data from project outcomes and interviews with key personnel, this paper analyzes the implementation and impact of LGC, offering insights and strategies for similar initiatives across Europe. The findings provide a foundation for a renewed framework and mandate for public television, emphasizing the role of audience participation in fostering trust and enhancing public service media’s relevance in contemporary society.

Global analysis of the proposed benefits from REDD+ projects: influencing factors and discrepancies

Abstract Reducing emissions from deforestation and forest degradation in developing countries (REDD+) was initially envisioned to incentivize forest protection through payments for ecosystem services (PES). Nevertheless, our global understanding of REDD+ project benefits proposed by proponents to local communities is limited. To address this gap, we ask: Globally, how do REDD+ projects propose to provide local community benefits? 
We use and update the international database on REDD+ projects (ID-RECCO) to analyze the REDD+ benefits proposed by 347 REDD+ projects in 55 countries. Logistic regression and chi-square tests explore relationships between the probability of a benefit type being proposed and influencing factors. Our global-level analysis can serve as a useful benchmark for empirical case studies.
Our findings show the diversity of benefits REDD+ projects propose Most REDD+ projects (89%) proposed a mix of monetary and non-monetary benefits; employment was proposed in > ¾ of project, while forest restrictions were proposed by 52% of REDD+ projects. Many projects proposed infrastructure (107) or services (181). Factors that influence the probability a benefit type was proposed by a project include carbon certification standards, de facto forest user types, and country. 
PES-style conditional payments are no longer emblematic of benefits from REDD+ projects to local communities. Instead, REDD+ projects typically propose employment, livelihood activities, tenure clarification, and public goods and services. Further empirical research is needed to understand how meaningful these benefits are to local people, and their contribution in producing effective and equitable project outcomes. National policy and legal frameworks on benefit sharing, more explicit standards on benefit sharing among carbon standards, and better risk management related to community expectations from REDD+ projects are important avenues to ensure REDD+ benefits are aligned with the needs and realities of local communities.

PROJECT COST ANALYSIS THROUGH EARNED VALUE METHOD ON THE INSTALLATION OF BAGGAGE HANDLING SYSTEM (BHS) PPUPG (PROJECT CASE STUDY AT SULTAN HASANUDIN INTERNATIONAL AIRPORT MAKASSAR)

This research is aimed at analyzing the cost performance of the Ppupg Baggage Handling System (BHS) Installation project at Sultan Hasanudin Makassar International Airport. This research focuses on two key questions: (1) how to assess cost performance during monitoring using indicators such as Cost Variance and Cost Performance Index, and (2) what the estimated total project cost will be upon completion, specifically through the Estimated All Completion (EAC). The research is conducted from October Week 2 to the field in January 2025 which began in stages from research preparation, initial survey, literature review, data collection, proposal preparation, data analysis, and thesis preparation. The research was conducted in the form of observations, namely, the PPUPG baggage handling system installation project. The study was conducted using a multidisciplinary approach. The analysis of the EAC serves to enhance understanding of projected total project costs, allowing for better financial forecasting and budget management. The findings from this study can help practitioners optimize budgeting processes and improve project outcomes, thereby advancing the overall understanding of cost management in infrastructure projects. Future research can contribute to more effective cost management strategies and improved project performance outcomes by integrating findings from both CPM analysis and comprehensive literature.

Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects.